COVID-19: Projecting the impact in Rohingya refugee camps and beyond

Shaun A Truelove; Orit Abrahim; Chiara Altare; Andrew Azman; Paul B Spiegel

We used a stochastic Susceptible Exposed Infectious Recovered (SEIR) model to simulate transmission in this population. We simulated epidemics under three potential scenarios with different values of the basic reproductive number, R 0 : 1) a low transmission scenario based on transmission levels in many of the Chinese provinces with elevated isolation and control practices and an R 0 similar to influenza (R 0 =1.5-2.0) 14 ; 2) a moderate transmission scenario that mirrors estimates in early stages of the outbreak in Wuhan, China (R 0 =2.0-3.0) 15 ; and, 3) a high transmission scenario where we assume that R 0 is increased by a factor of 1.65 (R 0 =3.3-5.0) compared to estimates from open community settings, as was observed during the 2017 diphtheria outbreak. 16 The R 0 in each of these scenarios falls within the 95% confidence interval of the current range of estimates for COVID-19. 17 We assumed an Erlang distributed serial interval (time between the onset of symptoms in infector-infectee pairs) with a mean of 6 days (standard deviation = 4.2). 18 The proportion of infections that result in severe disease was estimated using data from a closed population of contacts in Shenzhen, China, for which complete data on contact, age, test results, symptoms, and initial severity were available. 18 Using a Bayesian binomial model, we estimated the proportion of infections that were severe by 10-year age groups and then applied those estimates to the Kutupalong-Balukhali population to get an overall proportion of infections that will develop into severe disease in the specific population. These estimates do not account for differences in comorbidities, differing attack rates by age due to population mixing characteristics, or various other factors.